LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning

2026-01-29

AI Summary

Existing graph neural approaches struggle to effectively align modalities and fuse information in multimodal attributed graphs, often neglecting graph context, suppressing cross-modal interactions, and lacking adaptive utilization of topological structure. This work introduces Clifford algebra into multimodal graph learning for the first time and proposes a decoupled propagation-aggregation paradigm: it achieves context-aware high-order modality alignment through modality-aware geometric manifolds and designs an adaptive holographic aggregation mechanism based on geometric-grade energy and scale for effective fusion. The proposed method significantly outperforms state-of-the-art models across nine datasets, achieving leading performance on three types of graph tasks and three types of modality tasks.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recently, the rapid advancement of multimodal domains has driven a data-centric paradigm shift in graph ML, transitioning from text-attributed to multimodal-attributed graphs. This advancement significantly enhances data representation and expands the scope of graph downstream tasks, such as modality-oriented tasks, thereby improving the practical utility of graph ML. Despite its promise, limitations exist in the current neural paradigms: (1) Neglect Context in Modality Alignment: Most existing methods adopt topology-constrained or modality-specific operators as tokenizers. These aligners inevitably neglect graph context and inhibit modality interaction, resulting in suboptimal alignment. (2) Lack of Adaptation in Modality Fusion: Most existing methods are simple adaptations for 2-modality graphs and fail to adequately exploit aligned tokens equipped with topology priors during fusion, leading to poor generalizability and performance degradation. To address the above issues, we propose LION (c\underline{LI}ff\underline{O}rd \underline{N}eural paradigm) based on the Clifford algebra and decoupled graph neural paradigm (i.e., propagation-then-aggregation) to implement alignment-then-fusion in multimodal-attributed graphs. Specifically, we first construct a modality-aware geometric manifold grounded in Clifford algebra. This geometric-induced high-order graph propagation efficiently achieves modality interaction, facilitating modality alignment. Then, based on the geometric grade properties of aligned tokens, we propose adaptive holographic aggregation. This module integrates the energy and scale of geometric grades with learnable parameters to improve modality fusion. Extensive experiments on 9 datasets demonstrate that LION significantly outperforms SOTA baselines across 3 graph and 3 modality downstream tasks.

Problem

• multimodal-attributed graphs
• modality alignment
• modality fusion
• graph context
• topology priors

Innovation

• Clifford algebra
• multimodal-attributed graphs
• modality alignment
• adaptive holographic aggregation
• decoupled graph neural paradigm